{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting the necessary data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You just need to do this only once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2018-08-26 16:51:35--  ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/NA18489/exome_alignment/NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam\n",
      "           => ‘NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam’\n",
      "Resolving ftp.1000genomes.ebi.ac.uk (ftp.1000genomes.ebi.ac.uk)... 193.62.192.8\n",
      "Connecting to ftp.1000genomes.ebi.ac.uk (ftp.1000genomes.ebi.ac.uk)|193.62.192.8|:21... connected.\n",
      "Logging in as anonymous ... Logged in!\n",
      "==> SYST ... done.    ==> PWD ... done.\n",
      "==> TYPE I ... done.  ==> CWD (1) /vol1/ftp/phase3/data/NA18489/exome_alignment ... done.\n",
      "==> SIZE NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam ... 327067172\n",
      "==> PASV ... done.    ==> RETR NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam ... done.\n",
      "Length: 327067172 (312M) (unauthoritative)\n",
      "\n",
      "NA18489.chrom20.ILL 100%[===================>] 311.92M  9.92MB/s    in 34s     \n",
      "\n",
      "2018-08-26 16:52:11 (9.18 MB/s) - ‘NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam’ saved [327067172]\n",
      "\n",
      "--2018-08-26 16:52:11--  ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/NA18489/exome_alignment/NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam.bai\n",
      "           => ‘NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam.bai’\n",
      "Resolving ftp.1000genomes.ebi.ac.uk (ftp.1000genomes.ebi.ac.uk)... 193.62.192.8\n",
      "Connecting to ftp.1000genomes.ebi.ac.uk (ftp.1000genomes.ebi.ac.uk)|193.62.192.8|:21... connected.\n",
      "Logging in as anonymous ... Logged in!\n",
      "==> SYST ... done.    ==> PWD ... done.\n",
      "==> TYPE I ... done.  ==> CWD (1) /vol1/ftp/phase3/data/NA18489/exome_alignment ... done.\n",
      "==> SIZE NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam.bai ... 170688\n",
      "==> PASV ... done.    ==> RETR NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam.bai ... done.\n",
      "Length: 170688 (167K) (unauthoritative)\n",
      "\n",
      "NA18489.chrom20.ILL 100%[===================>] 166.69K   403KB/s    in 0.4s    \n",
      "\n",
      "2018-08-26 16:52:13 (403 KB/s) - ‘NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam.bai’ saved [170688]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!rm -f NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam 2>/dev/null\n",
    "!rm -f NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam.bai 2>/dev/null\n",
    "!wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/NA18489/exome_alignment/NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam\n",
    "!wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/NA18489/exome_alignment/NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam.bai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The recipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tiago/anaconda3/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/tiago/anaconda3/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "#pip install pysam\n",
    "from collections import defaultdict\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pysam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bam = pysam.AlignmentFile('NA18489.chrom20.ILLUMINA.bwa.YRI.exome.20121211.bam', 'rb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HD\n",
      "\t\tSO\n",
      "\t\tVN\n",
      "SQ\n",
      "\t1\n",
      "\t\tSN\t1\n",
      "\t\tM5\t1b22b98cdeb4a9304cb5d48026a85128\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t249250621\n",
      "\t2\n",
      "\t\tSN\t2\n",
      "\t\tM5\ta0d9851da00400dec1098a9255ac712e\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t243199373\n",
      "\t3\n",
      "\t\tSN\t3\n",
      "\t\tM5\tfdfd811849cc2fadebc929bb925902e5\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t198022430\n",
      "\t4\n",
      "\t\tSN\t4\n",
      "\t\tM5\t23dccd106897542ad87d2765d28a19a1\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t191154276\n",
      "\t5\n",
      "\t\tSN\t5\n",
      "\t\tM5\t0740173db9ffd264d728f32784845cd7\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t180915260\n",
      "\t6\n",
      "\t\tSN\t6\n",
      "\t\tM5\t1d3a93a248d92a729ee764823acbbc6b\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t171115067\n",
      "\t7\n",
      "\t\tSN\t7\n",
      "\t\tM5\t618366e953d6aaad97dbe4777c29375e\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t159138663\n",
      "\t8\n",
      "\t\tSN\t8\n",
      "\t\tM5\t96f514a9929e410c6651697bded59aec\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t146364022\n",
      "\t9\n",
      "\t\tSN\t9\n",
      "\t\tM5\t3e273117f15e0a400f01055d9f393768\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t141213431\n",
      "\t10\n",
      "\t\tSN\t10\n",
      "\t\tM5\t988c28e000e84c26d552359af1ea2e1d\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t135534747\n",
      "\t11\n",
      "\t\tSN\t11\n",
      "\t\tM5\t98c59049a2df285c76ffb1c6db8f8b96\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t135006516\n",
      "\t12\n",
      "\t\tSN\t12\n",
      "\t\tM5\t51851ac0e1a115847ad36449b0015864\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t133851895\n",
      "\t13\n",
      "\t\tSN\t13\n",
      "\t\tM5\t283f8d7892baa81b510a015719ca7b0b\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t115169878\n",
      "\t14\n",
      "\t\tSN\t14\n",
      "\t\tM5\t98f3cae32b2a2e9524bc19813927542e\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t107349540\n",
      "\t15\n",
      "\t\tSN\t15\n",
      "\t\tM5\te5645a794a8238215b2cd77acb95a078\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t102531392\n",
      "\t16\n",
      "\t\tSN\t16\n",
      "\t\tM5\tfc9b1a7b42b97a864f56b348b06095e6\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t90354753\n",
      "\t17\n",
      "\t\tSN\t17\n",
      "\t\tM5\t351f64d4f4f9ddd45b35336ad97aa6de\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t81195210\n",
      "\t18\n",
      "\t\tSN\t18\n",
      "\t\tM5\tb15d4b2d29dde9d3e4f93d1d0f2cbc9c\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t78077248\n",
      "\t19\n",
      "\t\tSN\t19\n",
      "\t\tM5\t1aacd71f30db8e561810913e0b72636d\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t59128983\n",
      "\t20\n",
      "\t\tSN\t20\n",
      "\t\tM5\t0dec9660ec1efaaf33281c0d5ea2560f\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t63025520\n",
      "\t21\n",
      "\t\tSN\t21\n",
      "\t\tM5\t2979a6085bfe28e3ad6f552f361ed74d\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t48129895\n",
      "\t22\n",
      "\t\tSN\t22\n",
      "\t\tM5\ta718acaa6135fdca8357d5bfe94211dd\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t51304566\n",
      "\t23\n",
      "\t\tSN\tX\n",
      "\t\tM5\t7e0e2e580297b7764e31dbc80c2540dd\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t155270560\n",
      "\t24\n",
      "\t\tSN\tY\n",
      "\t\tM5\t1fa3474750af0948bdf97d5a0ee52e51\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t59373566\n",
      "\t25\n",
      "\t\tSN\tMT\n",
      "\t\tM5\tc68f52674c9fb33aef52dcf399755519\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t16569\n",
      "\t26\n",
      "\t\tSN\tGL000207.1\n",
      "\t\tM5\tf3814841f1939d3ca19072d9e89f3fd7\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t4262\n",
      "\t27\n",
      "\t\tSN\tGL000226.1\n",
      "\t\tM5\t1c1b2cd1fccbc0a99b6a447fa24d1504\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t15008\n",
      "\t28\n",
      "\t\tSN\tGL000229.1\n",
      "\t\tM5\td0f40ec87de311d8e715b52e4c7062e1\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t19913\n",
      "\t29\n",
      "\t\tSN\tGL000231.1\n",
      "\t\tM5\tba8882ce3a1efa2080e5d29b956568a4\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t27386\n",
      "\t30\n",
      "\t\tSN\tGL000210.1\n",
      "\t\tM5\t851106a74238044126131ce2a8e5847c\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t27682\n",
      "\t31\n",
      "\t\tSN\tGL000239.1\n",
      "\t\tM5\t99795f15702caec4fa1c4e15f8a29c07\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t33824\n",
      "\t32\n",
      "\t\tSN\tGL000235.1\n",
      "\t\tM5\t118a25ca210cfbcdfb6c2ebb249f9680\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t34474\n",
      "\t33\n",
      "\t\tSN\tGL000201.1\n",
      "\t\tM5\tdfb7e7ec60ffdcb85cb359ea28454ee9\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t36148\n",
      "\t34\n",
      "\t\tSN\tGL000247.1\n",
      "\t\tM5\t7de00226bb7df1c57276ca6baabafd15\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t36422\n",
      "\t35\n",
      "\t\tSN\tGL000245.1\n",
      "\t\tM5\t89bc61960f37d94abf0df2d481ada0ec\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t36651\n",
      "\t36\n",
      "\t\tSN\tGL000197.1\n",
      "\t\tM5\t6f5efdd36643a9b8c8ccad6f2f1edc7b\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t37175\n",
      "\t37\n",
      "\t\tSN\tGL000203.1\n",
      "\t\tM5\t96358c325fe0e70bee73436e8bb14dbd\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t37498\n",
      "\t38\n",
      "\t\tSN\tGL000246.1\n",
      "\t\tM5\te4afcd31912af9d9c2546acf1cb23af2\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t38154\n",
      "\t39\n",
      "\t\tSN\tGL000249.1\n",
      "\t\tM5\t1d78abec37c15fe29a275eb08d5af236\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t38502\n",
      "\t40\n",
      "\t\tSN\tGL000196.1\n",
      "\t\tM5\td92206d1bb4c3b4019c43c0875c06dc0\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t38914\n",
      "\t41\n",
      "\t\tSN\tGL000248.1\n",
      "\t\tM5\t5a8e43bec9be36c7b49c84d585107776\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t39786\n",
      "\t42\n",
      "\t\tSN\tGL000244.1\n",
      "\t\tM5\t0996b4475f353ca98bacb756ac479140\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t39929\n",
      "\t43\n",
      "\t\tSN\tGL000238.1\n",
      "\t\tM5\t131b1efc3270cc838686b54e7c34b17b\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t39939\n",
      "\t44\n",
      "\t\tSN\tGL000202.1\n",
      "\t\tM5\t06cbf126247d89664a4faebad130fe9c\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t40103\n",
      "\t45\n",
      "\t\tSN\tGL000234.1\n",
      "\t\tM5\t93f998536b61a56fd0ff47322a911d4b\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t40531\n",
      "\t46\n",
      "\t\tSN\tGL000232.1\n",
      "\t\tM5\t3e06b6741061ad93a8587531307057d8\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t40652\n",
      "\t47\n",
      "\t\tSN\tGL000206.1\n",
      "\t\tM5\t43f69e423533e948bfae5ce1d45bd3f1\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t41001\n",
      "\t48\n",
      "\t\tSN\tGL000240.1\n",
      "\t\tM5\t445a86173da9f237d7bcf41c6cb8cc62\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t41933\n",
      "\t49\n",
      "\t\tSN\tGL000236.1\n",
      "\t\tM5\tfdcd739913efa1fdc64b6c0cd7016779\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t41934\n",
      "\t50\n",
      "\t\tSN\tGL000241.1\n",
      "\t\tM5\tef4258cdc5a45c206cea8fc3e1d858cf\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t42152\n",
      "\t51\n",
      "\t\tSN\tGL000243.1\n",
      "\t\tM5\tcc34279a7e353136741c9fce79bc4396\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t43341\n",
      "\t52\n",
      "\t\tSN\tGL000242.1\n",
      "\t\tM5\t2f8694fc47576bc81b5fe9e7de0ba49e\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t43523\n",
      "\t53\n",
      "\t\tSN\tGL000230.1\n",
      "\t\tM5\tb4eb71ee878d3706246b7c1dbef69299\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t43691\n",
      "\t54\n",
      "\t\tSN\tGL000237.1\n",
      "\t\tM5\te0c82e7751df73f4f6d0ed30cdc853c0\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t45867\n",
      "\t55\n",
      "\t\tSN\tGL000233.1\n",
      "\t\tM5\t7fed60298a8d62ff808b74b6ce820001\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t45941\n",
      "\t56\n",
      "\t\tSN\tGL000204.1\n",
      "\t\tM5\tefc49c871536fa8d79cb0a06fa739722\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t81310\n",
      "\t57\n",
      "\t\tSN\tGL000198.1\n",
      "\t\tM5\t868e7784040da90d900d2d1b667a1383\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t90085\n",
      "\t58\n",
      "\t\tSN\tGL000208.1\n",
      "\t\tM5\taa81be49bf3fe63a79bdc6a6f279abf6\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t92689\n",
      "\t59\n",
      "\t\tSN\tGL000191.1\n",
      "\t\tM5\td75b436f50a8214ee9c2a51d30b2c2cc\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t106433\n",
      "\t60\n",
      "\t\tSN\tGL000227.1\n",
      "\t\tM5\ta4aead23f8053f2655e468bcc6ecdceb\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t128374\n",
      "\t61\n",
      "\t\tSN\tGL000228.1\n",
      "\t\tM5\tc5a17c97e2c1a0b6a9cc5a6b064b714f\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t129120\n",
      "\t62\n",
      "\t\tSN\tGL000214.1\n",
      "\t\tM5\t46c2032c37f2ed899eb41c0473319a69\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t137718\n",
      "\t63\n",
      "\t\tSN\tGL000221.1\n",
      "\t\tM5\t3238fb74ea87ae857f9c7508d315babb\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t155397\n",
      "\t64\n",
      "\t\tSN\tGL000209.1\n",
      "\t\tM5\tf40598e2a5a6b26e84a3775e0d1e2c81\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t159169\n",
      "\t65\n",
      "\t\tSN\tGL000218.1\n",
      "\t\tM5\t1d708b54644c26c7e01c2dad5426d38c\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t161147\n",
      "\t66\n",
      "\t\tSN\tGL000220.1\n",
      "\t\tM5\tfc35de963c57bf7648429e6454f1c9db\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t161802\n",
      "\t67\n",
      "\t\tSN\tGL000213.1\n",
      "\t\tM5\t9d424fdcc98866650b58f004080a992a\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t164239\n",
      "\t68\n",
      "\t\tSN\tGL000211.1\n",
      "\t\tM5\t7daaa45c66b288847b9b32b964e623d3\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t166566\n",
      "\t69\n",
      "\t\tSN\tGL000199.1\n",
      "\t\tM5\t569af3b73522fab4b40995ae4944e78e\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t169874\n",
      "\t70\n",
      "\t\tSN\tGL000217.1\n",
      "\t\tM5\t6d243e18dea1945fb7f2517615b8f52e\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t172149\n",
      "\t71\n",
      "\t\tSN\tGL000216.1\n",
      "\t\tM5\t642a232d91c486ac339263820aef7fe0\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t172294\n",
      "\t72\n",
      "\t\tSN\tGL000215.1\n",
      "\t\tM5\t5eb3b418480ae67a997957c909375a73\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t172545\n",
      "\t73\n",
      "\t\tSN\tGL000205.1\n",
      "\t\tM5\td22441398d99caf673e9afb9a1908ec5\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t174588\n",
      "\t74\n",
      "\t\tSN\tGL000219.1\n",
      "\t\tM5\tf977edd13bac459cb2ed4a5457dba1b3\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t179198\n",
      "\t75\n",
      "\t\tSN\tGL000224.1\n",
      "\t\tM5\td5b2fc04f6b41b212a4198a07f450e20\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t179693\n",
      "\t76\n",
      "\t\tSN\tGL000223.1\n",
      "\t\tM5\t399dfa03bf32022ab52a846f7ca35b30\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t180455\n",
      "\t77\n",
      "\t\tSN\tGL000195.1\n",
      "\t\tM5\t5d9ec007868d517e73543b005ba48535\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t182896\n",
      "\t78\n",
      "\t\tSN\tGL000212.1\n",
      "\t\tM5\t563531689f3dbd691331fd6c5730a88b\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t186858\n",
      "\t79\n",
      "\t\tSN\tGL000222.1\n",
      "\t\tM5\t6fe9abac455169f50470f5a6b01d0f59\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t186861\n",
      "\t80\n",
      "\t\tSN\tGL000200.1\n",
      "\t\tM5\t75e4c8d17cd4addf3917d1703cacaf25\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t187035\n",
      "\t81\n",
      "\t\tSN\tGL000193.1\n",
      "\t\tM5\tdbb6e8ece0b5de29da56601613007c2a\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t189789\n",
      "\t82\n",
      "\t\tSN\tGL000194.1\n",
      "\t\tM5\t6ac8f815bf8e845bb3031b73f812c012\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t191469\n",
      "\t83\n",
      "\t\tSN\tGL000225.1\n",
      "\t\tM5\t63945c3e6962f28ffd469719a747e73c\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t211173\n",
      "\t84\n",
      "\t\tSN\tGL000192.1\n",
      "\t\tM5\t325ba9e808f669dfeee210fdd7b470ac\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t547496\n",
      "\t85\n",
      "\t\tSN\tNC_007605\n",
      "\t\tM5\t6743bd63b3ff2b5b8985d8933c53290a\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t171823\n",
      "\t86\n",
      "\t\tSN\ths37d5\n",
      "\t\tM5\t5b6a4b3a81a2d3c134b7d14bf6ad39f1\n",
      "\t\tUR\tftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz        AS:NCBI37       SP:Human\n",
      "\t\tLN\t35477943\n",
      "RG\n",
      "\t1\n",
      "\t\tSM\tNA18489\n",
      "\t\tLB\tSolexa-51039\n",
      "\t\tDS\tSRP004074\n",
      "\t\tID\tSRR100025\n",
      "\t\tPI\t220\n",
      "\t\tCN\tBI\n",
      "\t\tPL\tILLUMINA\n",
      "PG\n",
      "\t1\n",
      "\t\tVN\t0.5.9-r16\n",
      "\t\tID\tbwa_index\n",
      "\t\tPN\tbwa\n",
      "\t\tCL\tbwa index -a bwtsw $reference_fasta\n",
      "\t2\n",
      "\t\tPP\tbwa_index\n",
      "\t\tVN\t0.5.9-r16\n",
      "\t\tID\tbwa_aln_fastq\n",
      "\t\tPN\tbwa\n",
      "\t\tCL\tbwa aln -q 15 -f $sai_file $reference_fasta $fastq_file\n",
      "\t3\n",
      "\t\tPP\tbwa_aln_fastq\n",
      "\t\tVN\t0.5.9-r16\n",
      "\t\tID\tbwa_sam\n",
      "\t\tPN\tbwa\n",
      "\t\tCL\tbwa sampe -a 660 -r $rg_line -f $sam_file $reference_fasta $sai_file(s) $fastq_file(s)\n",
      "\t4\n",
      "\t\tPP\tbwa_sam\n",
      "\t\tVN\t0.1.17 (r973:277)\n",
      "\t\tID\tsam_to_fixed_bam\n",
      "\t\tPN\tsamtools\n",
      "\t\tCL\tsamtools view -bSu $sam_file | samtools sort -n -o - samtools_nsort_tmp | samtools fixmate /dev/stdin /dev/stdout | samtools sort -o - samtools_csort_tmp | samtools fillmd -u - $reference_fasta > $fixed_bam_file\n",
      "\t5\n",
      "\t\tPP\tsam_to_fixed_bam\n",
      "\t\tVN\t1.2-29-g0acaf2d\n",
      "\t\tID\tgatk_target_interval_creator\n",
      "\t\tPN\tGenomeAnalysisTK\n",
      "\t\tCL\tjava $jvm_args -jar GenomeAnalysisTK.jar -T RealignerTargetCreator -R $reference_fasta -o $intervals_file -known $known_indels_file(s) \n",
      "\t6\n",
      "\t\tPP\tgatk_target_interval_creator\n",
      "\t\tVN\t1.2-29-g0acaf2d\n",
      "\t\tID\tbam_realignment_around_known_indels\n",
      "\t\tPN\tGenomeAnalysisTK\n",
      "\t\tCL\tjava $jvm_args -jar GenomeAnalysisTK.jar -T IndelRealigner -R $reference_fasta -I $bam_file -o $realigned_bam_file -targetIntervals $intervals_file -known $known_indels_file(s) -LOD 0.4 -model KNOWNS_ONLY -compress 0 --disable_bam_indexing\n",
      "\t7\n",
      "\t\tPP\tbam_realignment_around_known_indels\n",
      "\t\tVN\t1.2-29-g0acaf2d\n",
      "\t\tID\tbam_count_covariates\n",
      "\t\tPN\tGenomeAnalysisTK\n",
      "\t\tCL\tjava $jvm_args -jar GenomeAnalysisTK.jar -T CountCovariates -R $reference_fasta -I $bam_file -recalFile $bam_file.recal_data.csv -knownSites $known_sites_file(s) -l INFO -L '1;2;3;4;5;6;7;8;9;10;11;12;13;14;15;16;17;18;19;20;21;22;X;Y;MT' -cov ReadGroupCovariate -cov QualityScoreCovariate -cov CycleCovariate -cov DinucCovariate\n",
      "\t8\n",
      "\t\tPP\tbam_count_covariates\n",
      "\t\tVN\t1.2-29-g0acaf2d\n",
      "\t\tID\tbam_recalibrate_quality_scores\n",
      "\t\tPN\tGenomeAnalysisTK\n",
      "\t\tCL\tjava $jvm_args -jar GenomeAnalysisTK.jar -T TableRecalibration -R $reference_fasta -recalFile $bam_file.recal_data.csv -I $bam_file -o $recalibrated_bam_file -l INFO -compress 0 --disable_bam_indexing\n",
      "\t9\n",
      "\t\tPP\tbam_recalibrate_quality_scores\n",
      "\t\tVN\t0.1.17 (r973:277)\n",
      "\t\tID\tbam_calculate_bq\n",
      "\t\tPN\tsamtools\n",
      "\t\tCL\tsamtools calmd -Erb $bam_file $reference_fasta > $bq_bam_file\n",
      "\t10\n",
      "\t\tPP\tbam_calculate_bq\n",
      "\t\tVN\t1.53\n",
      "\t\tID\tbam_merge\n",
      "\t\tPN\tpicard\n",
      "\t\tCL\tjava $jvm_args -jar MergeSamFiles.jar INPUT=$bam_file(s) OUTPUT=$merged_bam VALIDATION_STRINGENCY=SILENT\n",
      "\t11\n",
      "\t\tPP\tbam_merge\n",
      "\t\tVN\t1.53\n",
      "\t\tID\tbam_mark_duplicates\n",
      "\t\tPN\tpicard\n",
      "\t\tCL\tjava $jvm_args -jar MarkDuplicates.jar INPUT=$bam_file OUTPUT=$markdup_bam_file ASSUME_SORTED=TRUE METRICS_FILE=/dev/null VALIDATION_STRINGENCY=SILENT\n",
      "\t12\n",
      "\t\tPP\tbam_mark_duplicates\n",
      "\t\tVN\t1.53\n",
      "\t\tID\tbam_merge.1\n",
      "\t\tPN\tpicard\n",
      "\t\tCL\tjava $jvm_args -jar MergeSamFiles.jar INPUT=$bam_file(s) OUTPUT=$merged_bam VALIDATION_STRINGENCY=SILENT\n",
      "CO\n",
      "\t\t$known_indels_file(s) = ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_mapping_resources/ALL.wgs.indels_mills_devine_hg19_leftAligned_collapsed_double_hit.indels.sites.vcf.gz\n",
      "\t\t$known_indels_file(s) .= ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_mapping_resources/ALL.wgs.low_coverage_vqsr.20101123.indels.sites.vcf.gz\n",
      "\t\t$known_sites_file(s) = ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_mapping_resources/ALL.wgs.dbsnp.build135.snps.sites.vcf.gz\n"
     ]
    }
   ],
   "source": [
    "headers = bam.header\n",
    "for record_type, records in headers.items():\n",
    "    print (record_type)\n",
    "    for i, record in enumerate(records):\n",
    "        if type(record) == dict:\n",
    "            print('\\t%d' % (i + 1))\n",
    "            for field, value in record.items():\n",
    "                print('\\t\\t%s\\t%s' % (field, value))\n",
    "        else:\n",
    "            print('\\t\\t%s' % record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SRR100025.62130839 19 20 59996 60048\n",
      "52M24S\n",
      "0 52 52\n",
      "19 60228 295\n",
      "True True False 60\n",
      "array('B', [33, 34, 36, 33, 39, 34, 33, 38, 39, 34, 40, 35, 40, 40, 32, 40, 38, 33, 35, 38, 33, 39, 40, 34, 37, 39, 36, 30, 36, 37, 34, 35, 34, 40, 37, 34, 38, 28, 40, 40, 38, 32, 33, 32, 36, 34, 37, 24, 34, 35, 31, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])\n",
      "array('B', [33, 34, 36, 33, 39, 34, 33, 38, 39, 34, 40, 35, 40, 40, 32, 40, 38, 33, 35, 38, 33, 39, 40, 34, 37, 39, 36, 30, 36, 37, 34, 35, 34, 40, 37, 34, 38, 28, 40, 40, 38, 32, 33, 32, 36, 34, 37, 24, 34, 35, 31, 2])\n",
      "CTCAGATCCAGAGGTGGAAGAGGAAGGAAGCTTGGAACCCTATAGAGTTGCTGAGTGCCAGGACCAGATACTGGGC\n"
     ]
    }
   ],
   "source": [
    "#0-based\n",
    "for rec in bam:\n",
    "    if rec.cigarstring.find('M') > -1 and rec.cigarstring.find('S') > -1 and not rec.is_unmapped and not rec.mate_is_unmapped:\n",
    "        break\n",
    "print(rec.query_name, rec.reference_id, bam.getrname(rec.reference_id), rec.reference_start, rec.reference_end)\n",
    "print(rec.cigarstring)\n",
    "print(rec.query_alignment_start, rec.query_alignment_end, rec.query_alignment_length)\n",
    "print(rec.next_reference_id, rec.next_reference_start, rec.template_length)\n",
    "print(rec.is_paired, rec.is_proper_pair, rec.is_unmapped, rec.mapping_quality)\n",
    "print(rec.query_qualities)\n",
    "print(rec.query_alignment_qualities)\n",
    "print(rec.query_sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8140022eb8>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = [0] * 76\n",
    "for n, rec in enumerate(bam.fetch('20', 0, 10000000)):\n",
    "    for i in range(rec.query_alignment_start, rec.query_alignment_end):\n",
    "        counts[i] += 1\n",
    "freqs = [x / (n + 1.) for x in counts]\n",
    "fig, ax = plt.subplots(figsize=(16,9))\n",
    "ax.plot(range(1, 77), freqs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "phreds = defaultdict(list)\n",
    "for rec in bam.fetch('20', 0, None):\n",
    "    for i in range(rec.query_alignment_start, rec.query_alignment_end):\n",
    "        phreds[i].append(rec.query_qualities[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxs = [max(phreds[i]) for i in range(76)]\n",
    "tops = [np.percentile(phreds[i], 95) for i in range(76)]\n",
    "medians = [np.percentile(phreds[i], 50) for i in range(76)]\n",
    "bottoms = [np.percentile(phreds[i], 5) for i in range(76)]\n",
    "medians_fig = [x - y for x, y in zip(medians, bottoms)]\n",
    "tops_fig = [x - y for x, y in zip(tops, medians)]\n",
    "maxs_fig = [x - y for x, y in zip(maxs, tops)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f813fd52cc0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,9))\n",
    "ax.stackplot(range(1, 77), (bottoms, medians_fig, tops_fig, maxs_fig))\n",
    "ax.plot(range(1, 77), maxs, 'k-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
